Articles | Volume 18, issue 18
https://doi.org/10.5194/bg-18-5185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-5185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2021
Research article |
| 22 Sep 2021
| 22 Sep 2021
Assessing the response of soil carbon in Australia to changing inputs and climate using a consistent modelling framework
Juhwan Lee
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Raphael A. Viscarra Rossel
CORRESPONDING AUTHOR
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Mingxi Zhang
Soil and Landscape Science, School of Molecular and Life Sciences, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Zhongkui Luo
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China
Ying-Ping Wang
CSIRO Oceans and Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia
Related authors
Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-221, https://doi.org/10.5194/bg-2022-221, 2022
Preprint withdrawn
Short summary
Short summary
One of the least well understood processes in the nitrogen (N) cycle is the loss of nitrogen gas (N2), referred to as total denitrification. This is mainly due to the difficulty of quantifying total denitrification in situ. In this study, we developed and tested a novel modeling approach to estimate total denitrification over the depth profile, based on concentrations and isotope values of N2O. Our method will help close N budgets and identify management strategies that reduce N pollution.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
Short summary
Short summary
This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
C. Decock, J. Lee, M. Necpalova, E. I. P. Pereira, D. M. Tendall, and J. Six
SOIL, 1, 687–694, https://doi.org/10.5194/soil-1-687-2015, https://doi.org/10.5194/soil-1-687-2015, 2015
Short summary
Short summary
Further progress in understanding and mitigating N2O emissions from soil lies within transdisciplinary research that reaches across spatial scales and takes an ambitious look into the future.
Thorsten Behrens, Karsten Schmidt, Felix Stumpf, Simon Tutsch, Marie Hertzog, Urs Grob, Armin Keller, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2024-2810, https://doi.org/10.5194/egusphere-2024-2810, 2024
Short summary
Short summary
We integrate various methods to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their fieldwork. A new sampling design based on a geographical stratification is proposed focussing on local feature space variability. It allows for a systematic analysis of predictive accuracy for varying densities. The spectral and spatial models yielded high accuracies. Our study highlights the value of integrating pedometric technologies in soil surveys.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
Short summary
Short summary
Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
Short summary
Short summary
The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Lewis Walden, Farid Sepanta, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2023-2464, https://doi.org/10.5194/egusphere-2023-2464, 2023
Preprint archived
Short summary
Short summary
We characterised the chemical and mineral composition of soil organic carbon fractions with mid-infrared spectroscopy. We identified unique and shared features of the spectra of carbon fractions, and the interactions between their organic and mineral components. These interactions are key to the persistence of C in soils, and we propose that mid-infrared spectroscopy could help to infer stability of soil C.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
Short summary
Short summary
We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Charlotte Decock, Juhwan Lee, Matti Barthel, Elizabeth Verhoeven, Franz Conen, and Johan Six
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-221, https://doi.org/10.5194/bg-2022-221, 2022
Preprint withdrawn
Short summary
Short summary
One of the least well understood processes in the nitrogen (N) cycle is the loss of nitrogen gas (N2), referred to as total denitrification. This is mainly due to the difficulty of quantifying total denitrification in situ. In this study, we developed and tested a novel modeling approach to estimate total denitrification over the depth profile, based on concentrations and isotope values of N2O. Our method will help close N budgets and identify management strategies that reduce N pollution.
Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
SOIL, 8, 467–486, https://doi.org/10.5194/soil-8-467-2022, https://doi.org/10.5194/soil-8-467-2022, 2022
Short summary
Short summary
We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
Short summary
Short summary
We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
Short summary
Short summary
Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Philipp Baumann, Juhwan Lee, Emmanuel Frossard, Laurie Paule Schönholzer, Lucien Diby, Valérie Kouamé Hgaza, Delwende Innocent Kiba, Andrew Sila, Keith Sheperd, and Johan Six
SOIL, 7, 717–731, https://doi.org/10.5194/soil-7-717-2021, https://doi.org/10.5194/soil-7-717-2021, 2021
Short summary
Short summary
This work delivers openly accessible and validated calibrations for diagnosing 26 soil properties based on mid-infrared spectroscopy. These were developed for four regions in Burkina Faso and Côte d'Ivoire, including 80 fields of smallholder farmers. The models can help to site-specifically and cost-efficiently monitor soil quality and fertility constraints to ameliorate soils and yields of yam or other staple crops in the four regions between the humid forest and the northern Guinean savanna.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
Short summary
Short summary
We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
Short summary
Short summary
In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
Short summary
Short summary
Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Andre Carnieletto Dotto, Jose A. M. Demattê, Raphael A. Viscarra Rossel, and Rodnei Rizzo
SOIL, 6, 163–177, https://doi.org/10.5194/soil-6-163-2020, https://doi.org/10.5194/soil-6-163-2020, 2020
Short summary
Short summary
The objective of this study was to develop a soil grouping system based on spectral, climate, and terrain variables with the aim of developing a quantitative way to classify soils. To derive the new system, we applied the above-mentioned variables using cluster analysis and defined eight groups or "soil environment groupings" (SEGs). The SEG system facilitated the identification of groups with similar characteristics using not only soil but also environmental variables for their distinction.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences, 16, 3069–3093, https://doi.org/10.5194/bg-16-3069-2019, https://doi.org/10.5194/bg-16-3069-2019, 2019
Short summary
Short summary
This study presents an estimate of global terrestrial photosynthesis. We make use of satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and estimate photosynthetic carbon uptake. This new framework incorporates nonlinear, process-based understanding of the link between fluorescence and photosynthesis, an advance on past approaches. This will aid in the utility of fluorescence to quantify terrestrial carbon cycle feedbacks.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
Short summary
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
Short summary
Short summary
Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
Short summary
Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Juhwan Lee, Gina M. Garland, and Raphael A. Viscarra Rossel
SOIL, 4, 213–224, https://doi.org/10.5194/soil-4-213-2018, https://doi.org/10.5194/soil-4-213-2018, 2018
Short summary
Short summary
Soil nitrogen (N) is an essential element for plant growth, but its plant-available forms are subject to loss from the environment by leaching and gaseous emissions. Still, factors controlling soil mineral N concentrations at large spatial scales are not well understood. We determined and discussed primary soil controls over the concentrations of NH4+ and NO3− at the continental scale of Australia while considering specific dominant land use patterns on a regional basis.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-270, https://doi.org/10.5194/bg-2018-270, 2018
Revised manuscript has not been submitted
Short summary
Short summary
This study presents a global estimate of land carbon uptake through photosynthesis. We make use satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and then use the optimized model to estimate photosynthetic carbon uptake. This provides a new tool that can combine measurements and observations in a systematic way and maximise the use of chlorophyll fluorescence to improve our understanding of the land carbon cycle.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
Short summary
Short summary
Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Jacqueline R. England and Raphael A. Viscarra Rossel
SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, https://doi.org/10.5194/soil-4-101-2018, 2018
Short summary
Short summary
Proximal sensing can be used for soil C accounting, but the methods need to be standardized and procedural guidelines developed to ensure proficient measurement and accurate reporting. This is particularly important if there are financial incentives for landholders to adopt practices to sequester C. We review sensing for C accounting and discuss the requirements for the development of new soil C accounting methods based on sensing, including requirements for reporting, auditing and verification.
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gabriel Abramowitz, Martin G. De Kauwe, Bradley Evans, Vanessa Haverd, Longhui Li, Caitlin Moore, Youngryel Ryu, Simon Scheiter, Stanislaus J. Schymanski, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, https://doi.org/10.5194/bg-14-4711-2017, 2017
Short summary
Short summary
This paper attempts to review some of the current challenges faced by the modelling community in simulating the behaviour of savanna ecosystems. We provide a particular focus on three dynamic processes (phenology, root-water access, and fire) that are characteristic of savannas, which we believe are not adequately represented in current-generation terrestrial biosphere models. We highlight reasons for these misrepresentations, possible solutions and a future direction for research in this area.
Rachel M. Law, Tilo Ziehn, Richard J. Matear, Andrew Lenton, Matthew A. Chamberlain, Lauren E. Stevens, Ying-Ping Wang, Jhan Srbinovsky, Daohua Bi, Hailin Yan, and Peter F. Vohralik
Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, https://doi.org/10.5194/gmd-10-2567-2017, 2017
Short summary
Short summary
The paper describes a version of the Australian Community Climate and Earth System Simulator that has been enabled to simulate the carbon cycle, which is designated ACCESS-ESM1. The model performance for pre-industrial conditions is assessed and land and ocean carbon fluxes are found to be simulated realistically.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Eva A. Kowalczyk, Lauren E. Stevens, Rachel M. Law, Ian N. Harman, Martin Dix, Charmaine N. Franklin, and Ying-Ping Wang
Geosci. Model Dev., 9, 2771–2791, https://doi.org/10.5194/gmd-9-2771-2016, https://doi.org/10.5194/gmd-9-2771-2016, 2016
Short summary
Short summary
This paper compares two ACCESS model versions that differ only in their land surface scheme. Differences in the simulated present-day climate are attributed to differences in the representation of various land surface processes.
Rashid Rafique, Jianyang Xia, Oleksandra Hararuk, Ghassem R. Asrar, Guoyong Leng, Yingping Wang, and Yiqi Luo
Earth Syst. Dynam., 7, 649–658, https://doi.org/10.5194/esd-7-649-2016, https://doi.org/10.5194/esd-7-649-2016, 2016
Short summary
Short summary
Traceability analysis was used to diagnose the causes of differences in simulating ecosystem carbon storage capacity between two land models: CLMA-CASA and CABLE. Results showed that the simulated ecosystem carbon storage capacity is largely influenced by the photosynthesis parameterization, residence time and organic matter decomposition.
Anna M. Ukkola, Andy J. Pitman, Mark Decker, Martin G. De Kauwe, Gab Abramowitz, Jatin Kala, and Ying-Ping Wang
Hydrol. Earth Syst. Sci., 20, 2403–2419, https://doi.org/10.5194/hess-20-2403-2016, https://doi.org/10.5194/hess-20-2403-2016, 2016
Rhys Whitley, Jason Beringer, Lindsay B. Hutley, Gab Abramowitz, Martin G. De Kauwe, Remko Duursma, Bradley Evans, Vanessa Haverd, Longhui Li, Youngryel Ryu, Benjamin Smith, Ying-Ping Wang, Mathew Williams, and Qiang Yu
Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, https://doi.org/10.5194/bg-13-3245-2016, 2016
Short summary
Short summary
In this study we assess how well terrestrial biosphere models perform at predicting water and carbon cycling for savanna ecosystems. We apply our models to five savanna sites in Northern Australia and highlight key causes for model failure. Our assessment of model performance uses a novel benchmarking system that scores a model’s predictive ability based on how well it is utilizing its driving information. On average, we found the models as a group display only moderate levels of performance.
Y. P. Wang, J. Jiang, B. Chen-Charpentier, F. B. Agusto, A. Hastings, F. Hoffman, M. Rasmussen, M. J. Smith, K. Todd-Brown, Y. Wang, X. Xu, and Y. Q. Luo
Biogeosciences, 13, 887–902, https://doi.org/10.5194/bg-13-887-2016, https://doi.org/10.5194/bg-13-887-2016, 2016
Short summary
Short summary
Comparing two nonlinear microbial models, we found that,
in response to warming, soil C decreases in one model but can increase or decrease in the other model, and sensitivity of priming response to carbon input increases with soil T in one model but decreases in the other model
Significance: these differences in the responses can be used to discern which model is more realistic, which will improve our understanding of the significance of soil microbial processes in the terrestrial C cycle.
M. G. De Kauwe, S.-X. Zhou, B. E. Medlyn, A. J. Pitman, Y.-P. Wang, R. A. Duursma, and I. C. Prentice
Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, https://doi.org/10.5194/bg-12-7503-2015, 2015
Short summary
Short summary
Future climate change has the potential to increase drought in many regions of the globe. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art models currently assume the same drought sensitivity for all vegetation. Our results indicate that models will over-estimate drought impacts in drier climates unless different sensitivity of vegetation to drought is taken into account.
C. Decock, J. Lee, M. Necpalova, E. I. P. Pereira, D. M. Tendall, and J. Six
SOIL, 1, 687–694, https://doi.org/10.5194/soil-1-687-2015, https://doi.org/10.5194/soil-1-687-2015, 2015
Short summary
Short summary
Further progress in understanding and mitigating N2O emissions from soil lies within transdisciplinary research that reaches across spatial scales and takes an ambitious look into the future.
J. Kala, M. G. De Kauwe, A. J. Pitman, R. Lorenz, B. E. Medlyn, Y.-P Wang, Y.-S Lin, and G. Abramowitz
Geosci. Model Dev., 8, 3877–3889, https://doi.org/10.5194/gmd-8-3877-2015, https://doi.org/10.5194/gmd-8-3877-2015, 2015
Short summary
Short summary
We implement a new stomatal conductance scheme within a land surface model coupled to a global climate model. The new model differs from the default in that it allows model parameters to vary by the different plant functional types, derived from global synthesis of observations. We show that the new scheme results in improvements in the model climatology and improves existing biases in warm temperature extremes by up to 10-20% over the boreal forests during summer.
I. C. Prentice, X. Liang, B. E. Medlyn, and Y.-P. Wang
Atmos. Chem. Phys., 15, 5987–6005, https://doi.org/10.5194/acp-15-5987-2015, https://doi.org/10.5194/acp-15-5987-2015, 2015
Short summary
Short summary
Land surface models (LSMs) describe how carbon and water fluxes react to environmental change. They are key component of climate models, yet they differ enormously. Many perform poorly, despite having many parameters. We outline a development strategy emphasizing robustness, reliability and realism, none of which is guaranteed by complexity alone. We propose multiple constraints, benchmarking and data assimilation, and representing unresolved processes stochastically, as tools in this endeavour.
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
Short summary
Short summary
Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
M. G. De Kauwe, J. Kala, Y.-S. Lin, A. J. Pitman, B. E. Medlyn, R. A. Duursma, G. Abramowitz, Y.-P. Wang, and D. G. Miralles
Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, https://doi.org/10.5194/gmd-8-431-2015, 2015
Short summary
Short summary
Stomatal conductance affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the CABLE land surface model (LSM). The new implementation resulted in a large reduction in the annual fluxes of transpiration across evergreen needleleaf, tundra and C4 grass regions. We conclude that optimisation theory can yield a tractable approach to predicting stomatal conductance in LSMs.
Y. P. Wang, B. C. Chen, W. R. Wieder, M. Leite, B. E. Medlyn, M. Rasmussen, M. J. Smith, F. B. Agusto, F. Hoffman, and Y. Q. Luo
Biogeosciences, 11, 1817–1831, https://doi.org/10.5194/bg-11-1817-2014, https://doi.org/10.5194/bg-11-1817-2014, 2014
J.-F. Exbrayat, A. J. Pitman, Q. Zhang, G. Abramowitz, and Y.-P. Wang
Biogeosciences, 10, 7095–7108, https://doi.org/10.5194/bg-10-7095-2013, https://doi.org/10.5194/bg-10-7095-2013, 2013
Q. Zhang, A. J. Pitman, Y. P. Wang, Y. J. Dai, and P. J. Lawrence
Earth Syst. Dynam., 4, 333–345, https://doi.org/10.5194/esd-4-333-2013, https://doi.org/10.5194/esd-4-333-2013, 2013
V. Haverd, M. R. Raupach, P. R. Briggs, J. G. Canadell, P. Isaac, C. Pickett-Heaps, S. H. Roxburgh, E. van Gorsel, R. A. Viscarra Rossel, and Z. Wang
Biogeosciences, 10, 2011–2040, https://doi.org/10.5194/bg-10-2011-2013, https://doi.org/10.5194/bg-10-2011-2013, 2013
Related subject area
Biogeochemistry: Land
Cropland expansion drives vegetation greenness decline in Southeast Asia
How to measure the efficiency of bioenergy crops compared to forestation
Implications of climate and litter quality for simulations of litterbag decomposition at high latitudes
Soil carbon-concentration and carbon-climate feedbacks in CMIP6 Earth system models
Monitoring the impact of forest changes on carbon uptake with solar-induced fluorescence measurements from GOME-2A and TROPOMI for an Australian and Chinese case study
Technical note: Flagging inconsistencies in flux tower data
Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning
Comparison of shortwave radiation dynamics between boreal forest and open peatland pairs in southern and northern Finland
High-resolution spatial patterns and drivers of terrestrial ecosystem carbon dioxide, methane, and nitrous oxide fluxes in the tundra
Long-term additions of ammonium nitrate to montane forest ecosystems may cause limited soil acidification, even in the presence of soil carbonate
Leaf carbon and nitrogen stoichiometric variation along environmental gradients
Gross primary productivity and the predictability of CO2: more uncertainty in what we predict than how well we predict it
Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model–data fusion
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
Carbon cycle extremes accelerate weakening of the land carbon sink in the late 21st century
Estimating oil-palm Si storage, Si return to soils, and Si losses through harvest in smallholder oil-palm plantations of Sumatra, Indonesia
Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Seasonal variation of mercury concentration of ancient olive groves of Lebanon
Soil organic matter diagenetic state informs boreal forest ecosystem feedbacks to climate change
Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing
Sun-induced fluorescence as a proxy for primary productivity across vegetation types and climates
Technical note: A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set
Changing sub-Arctic tundra vegetation upon permafrost degradation: impact on foliar mineral element cycling
Land Management Contributes significantly to observed Vegetation Browning in Syria during 2001–2018
MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas
Assessing the representation of the Australian carbon cycle in global vegetation models
Reviews and syntheses: Ongoing and emerging opportunities to improve environmental science using observations from the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites
First pan-Arctic assessment of dissolved organic carbon in lakes of the permafrost region
The impact of wildfire on biogeochemical fluxes and water quality in boreal catchments
Examining the sensitivity of the terrestrial carbon cycle to the expression of El Niño
Subalpine grassland productivity increased with warmer and drier conditions, but not with higher N deposition, in an altitudinal transplantation experiment
Reviews and syntheses: Impacts of plant-silica–herbivore interactions on terrestrial biogeochemical cycling
Implementation of nitrogen cycle in the CLASSIC land model
Combined effects of ozone and drought stress on the emission of biogenic volatile organic compounds from Quercus robur L.
A bottom-up quantification of foliar mercury uptake fluxes across Europe
Lagged effects regulate the inter-annual variability of the tropical carbon balance
Spatial variations in terrestrial net ecosystem productivity and its local indicators
Nitrogen cycling in CMIP6 land surface models: progress and limitations
Decomposing reflectance spectra to track gross primary production in a subalpine evergreen forest
Sensitivity of 21st century simulated ecosystem indicators to model parameters, prescribed climate drivers, RCP scenarios and forest management actions for two Finnish boreal forest sites
Summarizing the state of the terrestrial biosphere in few dimensions
Patterns and trends of the dominant environmental controls of net biome productivity
Localized basal area affects soil respiration temperature sensitivity in a coastal deciduous forest
Dissolved organic carbon mobilized from organic horizons of mature and harvested black spruce plots in a mesic boreal region
Ideas and perspectives: Proposed best practices for collaboration at cross-disciplinary observatories
Effects of leaf length and development stage on the triple oxygen isotope signature of grass leaf water and phytoliths: insights for a proxy of continental atmospheric humidity
Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models
Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar
Theoretical uncertainties for global satellite-derived burned area estimates
Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model
Ruiying Zhao, Xiangzhong Luo, Yuheng Yang, Luri Nurlaila Syahid, Chi Chen, and Janice Ser Huay Lee
Biogeosciences, 21, 5393–5406, https://doi.org/10.5194/bg-21-5393-2024, https://doi.org/10.5194/bg-21-5393-2024, 2024
Short summary
Short summary
Southeast Asia has been a global hot spot of land-use change over the past 50 years. Meanwhile, it also hosts some of the most carbon-dense and diverse ecosystems in the world. Here, we explore the impact of land-use change, along with other environmental factors, on the ecosystem in Southeast Asia. We find that elevated CO2 imposed a positive impact on vegetation greenness, but the positive impact was largely offset by intensive land-use changes in the region, particularly cropland expansion.
Sabine Egerer, Stefanie Falk, Dorothea Mayer, Tobias Nützel, Wolfgang A. Obermeier, and Julia Pongratz
Biogeosciences, 21, 5005–5025, https://doi.org/10.5194/bg-21-5005-2024, https://doi.org/10.5194/bg-21-5005-2024, 2024
Short summary
Short summary
Using a state-of-the-art land model, we find that bioenergy plants can store carbon more efficiently than forests over long periods in the soil, in geological reservoirs, or by substituting fossil-fuel-based energy. Planting forests is more suitable for reaching climate targets by 2050. The carbon removal potential depends also on local environmental conditions. These considerations have important implications for climate policy, spatial planning, nature conservation, and agriculture.
Elin Ristorp Aas, Inge Althuizen, Hui Tang, Sonya Geange, Eva Lieungh, Vigdis Vandvik, and Terje Koren Berntsen
Biogeosciences, 21, 3789–3817, https://doi.org/10.5194/bg-21-3789-2024, https://doi.org/10.5194/bg-21-3789-2024, 2024
Short summary
Short summary
We used a soil model to replicate two litterbag decomposition experiments to examine the implications of climate, litter quality, and soil microclimate representation. We found that macroclimate was more important than litter quality for modeled mass loss. By comparing different representations of soil temperature and moisture we found that using observed data did not improve model results. We discuss causes for this and suggest possible improvements to both the model and experimental design.
Rebecca M. Varney, Pierre Friedlingstein, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
Biogeosciences, 21, 2759–2776, https://doi.org/10.5194/bg-21-2759-2024, https://doi.org/10.5194/bg-21-2759-2024, 2024
Short summary
Short summary
Soil carbon is the largest store of carbon on the land surface of Earth and is known to be particularly sensitive to climate change. Understanding this future response is vital to successfully meeting Paris Agreement targets, which rely heavily on carbon uptake by the land surface. In this study, the individual responses of soil carbon are quantified and compared amongst CMIP6 Earth system models used within the most recent IPCC report, and the role of soils in the land response is highlighted.
Juliëtte C. S. Anema, Klaas Folkert Boersma, Piet Stammes, Gerbrand Koren, William Woodgate, Philipp Köhler, Christian Frankenberg, and Jacqui Stol
Biogeosciences, 21, 2297–2311, https://doi.org/10.5194/bg-21-2297-2024, https://doi.org/10.5194/bg-21-2297-2024, 2024
Short summary
Short summary
To keep the Paris agreement goals within reach, negative emissions are necessary. They can be achieved with mitigation techniques, such as reforestation, which remove CO2 from the atmosphere. While governments have pinned their hopes on them, there is not yet a good set of tools to objectively determine whether negative emissions do what they promise. Here we show how satellite measurements of plant fluorescence are useful in detecting carbon uptake due to reforestation and vegetation regrowth.
Martin Jung, Jacob Nelson, Mirco Migliavacca, Tarek El-Madany, Dario Papale, Markus Reichstein, Sophia Walther, and Thomas Wutzler
Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, https://doi.org/10.5194/bg-21-1827-2024, 2024
Short summary
Short summary
We present a methodology to detect inconsistencies in perhaps the most important data source for measurements of ecosystem–atmosphere carbon, water, and energy fluxes. We expect that the derived consistency flags will be relevant for data users and will help in improving our understanding of and our ability to model ecosystem–climate interactions.
Prajwal Khanal, Anne J. Hoek Van Dijke, Timo Schaffhauser, Wantong Li, Sinikka J. Paulus, Chunhui Zhan, and René Orth
Biogeosciences, 21, 1533–1547, https://doi.org/10.5194/bg-21-1533-2024, https://doi.org/10.5194/bg-21-1533-2024, 2024
Short summary
Short summary
Water availability is essential for vegetation functioning, but the depth of vegetation water uptake is largely unknown due to sparse ground measurements. This study correlates vegetation growth with soil moisture availability globally to infer vegetation water uptake depth using only satellite-based data. We find that the vegetation water uptake depth varies across climate regimes and vegetation types and also changes during dry months at a global scale.
Otso Peräkylä, Erkka Rinne, Ekaterina Ezhova, Anna Lintunen, Annalea Lohila, Juho Aalto, Mika Aurela, Pasi Kolari, and Markku Kulmala
EGUsphere, https://doi.org/10.5194/egusphere-2024-712, https://doi.org/10.5194/egusphere-2024-712, 2024
Short summary
Short summary
Forests are seen as beneficial for climate. Yet, in areas with snow, trees break up the white snow surface, and absorb more sunlight than open areas. This has a warming effect, negating some of the climate benefit of trees. We studied two pairs of an open peatland and a forest in Finland. We found that the later the snow melts, the larger the difference in absorbed sunlight between forests and peatlands. This has implications for the future, as snow cover duration is affected by global warming.
Anna-Maria Virkkala, Pekka Niittynen, Julia Kemppinen, Maija E. Marushchak, Carolina Voigt, Geert Hensgens, Johanna Kerttula, Konsta Happonen, Vilna Tyystjärvi, Christina Biasi, Jenni Hultman, Janne Rinne, and Miska Luoto
Biogeosciences, 21, 335–355, https://doi.org/10.5194/bg-21-335-2024, https://doi.org/10.5194/bg-21-335-2024, 2024
Short summary
Short summary
Arctic greenhouse gas (GHG) fluxes of CO2, CH4, and N2O are important for climate feedbacks. We combined extensive in situ measurements and remote sensing data to develop machine-learning models to predict GHG fluxes at a 2 m resolution across a tundra landscape. The analysis revealed that the system was a net GHG sink and showed widespread CH4 uptake in upland vegetation types, almost surpassing the high wetland CH4 emissions at the landscape scale.
Thomas Baer, Gerhard Furrer, Stephan Zimmermann, and Patrick Schleppi
Biogeosciences, 20, 4577–4589, https://doi.org/10.5194/bg-20-4577-2023, https://doi.org/10.5194/bg-20-4577-2023, 2023
Short summary
Short summary
Nitrogen (N) deposition to forest ecosystems is a matter of concern because it affects their nutrient status and makes their soil acidic. We observed an ongoing acidification in a montane forest in central Switzerland even if the subsoil of this site contains carbonates and is thus well buffered. We experimentally added N to simulate a higher pollution, and this increased the acidification. After 25 years of study, however, we can see the first signs of recovery, also under higher N deposition.
Huiying Xu, Han Wang, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 4511–4525, https://doi.org/10.5194/bg-20-4511-2023, https://doi.org/10.5194/bg-20-4511-2023, 2023
Short summary
Short summary
Leaf carbon (C) and nitrogen (N) are crucial elements in leaf construction and physiological processes. This study reconciled the roles of phylogeny, species identity, and climate in stoichiometric traits at individual and community levels. The variations in community-level leaf N and C : N ratio were captured by optimality-based models using climate data. Our results provide an approach to improve the representation of leaf stoichiometry in vegetation models to better couple N with C cycling.
István Dunkl, Nicole Lovenduski, Alessio Collalti, Vivek K. Arora, Tatiana Ilyina, and Victor Brovkin
Biogeosciences, 20, 3523–3538, https://doi.org/10.5194/bg-20-3523-2023, https://doi.org/10.5194/bg-20-3523-2023, 2023
Short summary
Short summary
Despite differences in the reproduction of gross primary productivity (GPP) by Earth system models (ESMs), ESMs have similar predictability of the global carbon cycle. We found that, although GPP variability originates from different regions and is driven by different climatic variables across the ESMs, the ESMs rely on the same mechanisms to predict their own GPP. This shows that the predictability of the carbon cycle is limited by our understanding of variability rather than predictability.
David T. Milodowski, T. Luke Smallman, and Mathew Williams
Biogeosciences, 20, 3301–3327, https://doi.org/10.5194/bg-20-3301-2023, https://doi.org/10.5194/bg-20-3301-2023, 2023
Short summary
Short summary
Model–data fusion (MDF) allows us to combine ecosystem models with Earth observation data. Fragmented landscapes, with a mosaic of contrasting ecosystems, pose a challenge for MDF. We develop a novel MDF framework to estimate the carbon balance of fragmented landscapes and show the importance of accounting for ecosystem heterogeneity to prevent scale-dependent bias in estimated carbon fluxes, disturbance fluxes in particular, and to improve ecological fidelity of the calibrated models.
Keri L. Bowering, Kate A. Edwards, and Susan E. Ziegler
Biogeosciences, 20, 2189–2206, https://doi.org/10.5194/bg-20-2189-2023, https://doi.org/10.5194/bg-20-2189-2023, 2023
Short summary
Short summary
Dissolved organic matter (DOM) mobilized from surface soils is a source of carbon (C) for deeper mineral horizons but also a mechanism of C loss. Composition of DOM mobilized in boreal forests varied more by season than as a result of forest harvesting. Results suggest reduced snowmelt and increased fall precipitation enhance DOM properties promoting mineral soil C stores. These findings, coupled with hydrology, can inform on soil C fate and boreal forest C balance in response to climate change.
Bharat Sharma, Jitendra Kumar, Auroop R. Ganguly, and Forrest M. Hoffman
Biogeosciences, 20, 1829–1841, https://doi.org/10.5194/bg-20-1829-2023, https://doi.org/10.5194/bg-20-1829-2023, 2023
Short summary
Short summary
Rising atmospheric carbon dioxide increases vegetation growth and causes more heatwaves and droughts. The impact of such climate extremes is detrimental to terrestrial carbon uptake capacity. We found that due to overall climate warming, about 88 % of the world's regions towards the end of 2100 will show anomalous losses in net biospheric productivity (NBP) rather than gains. More than 50 % of all negative NBP extremes were driven by the compound effect of dry, hot, and fire conditions.
Britta Greenshields, Barbara von der Lühe, Felix Schwarz, Harold J. Hughes, Aiyen Tjoa, Martyna Kotowska, Fabian Brambach, and Daniela Sauer
Biogeosciences, 20, 1259–1276, https://doi.org/10.5194/bg-20-1259-2023, https://doi.org/10.5194/bg-20-1259-2023, 2023
Short summary
Short summary
Silicon (Si) can have multiple beneficial effects on crops such as oil palms. In this study, we quantified Si concentrations in various parts of an oil palm (leaflets, rachises, fruit-bunch parts) to derive Si storage estimates for the total above-ground biomass of an oil palm and 1 ha of an oil-palm plantation. We proposed a Si balance by identifying Si return (via palm fronds) and losses (via harvest) in the system and recommend management measures that enhance Si cycling.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Nagham Tabaja, David Amouroux, Lamis Chalak, François Fourel, Emmanuel Tessier, Ihab Jomaa, Milad El Riachy, and Ilham Bentaleb
Biogeosciences, 20, 619–633, https://doi.org/10.5194/bg-20-619-2023, https://doi.org/10.5194/bg-20-619-2023, 2023
Short summary
Short summary
This study investigates the seasonality of the mercury (Hg) concentration of olive trees. Hg concentrations of foliage, stems, soil surface, and litter were analyzed on a monthly basis in ancient olive trees growing in two groves in Lebanon. Our study draws an adequate baseline for the eastern Mediterranean and for the region with similar climatic inventories on Hg vegetation uptake in addition to being a baseline for new studies on olive trees in the Mediterranean.
Allison N. Myers-Pigg, Karl Kaiser, Ronald Benner, and Susan E. Ziegler
Biogeosciences, 20, 489–503, https://doi.org/10.5194/bg-20-489-2023, https://doi.org/10.5194/bg-20-489-2023, 2023
Short summary
Short summary
Boreal forests, historically a global sink for atmospheric CO2, store carbon in vast soil reservoirs. To predict how such stores will respond to climate warming we need to understand climate–ecosystem feedbacks. We find boreal forest soil carbon stores are maintained through enhanced nitrogen cycling with climate warming, providing direct evidence for a key feedback. Further application of the approach demonstrated here will improve our understanding of the limits of climate–ecosystem feedbacks.
Matthew P. Dannenberg, Mallory L. Barnes, William K. Smith, Miriam R. Johnston, Susan K. Meerdink, Xian Wang, Russell L. Scott, and Joel A. Biederman
Biogeosciences, 20, 383–404, https://doi.org/10.5194/bg-20-383-2023, https://doi.org/10.5194/bg-20-383-2023, 2023
Short summary
Short summary
Earth's drylands provide ecosystem services to many people and will likely be strongly affected by climate change, but it is quite challenging to monitor the productivity and water use of dryland plants with satellites. We developed and tested an approach for estimating dryland vegetation activity using machine learning to combine information from multiple satellite sensors. Our approach excelled at estimating photosynthesis and water use largely due to the inclusion of satellite soil moisture.
Mark Pickering, Alessandro Cescatti, and Gregory Duveiller
Biogeosciences, 19, 4833–4864, https://doi.org/10.5194/bg-19-4833-2022, https://doi.org/10.5194/bg-19-4833-2022, 2022
Short summary
Short summary
This study explores two of the most recent products in carbon productivity estimation, FLUXCOM gross primary productivity (GPP), calculated by upscaling local measurements of CO2 exchange, and remotely sensed sun-induced chlorophyll a fluorescence (SIF). High-resolution SIF data are valuable in demonstrating similarity in the SIF–GPP relationship between vegetation covers, provide an independent probe of the FLUXCOM GPP model and demonstrate the response of SIF to meteorological fluctuations.
Sophia Walther, Simon Besnard, Jacob Allen Nelson, Tarek Sebastian El-Madany, Mirco Migliavacca, Ulrich Weber, Nuno Carvalhais, Sofia Lorena Ermida, Christian Brümmer, Frederik Schrader, Anatoly Stanislavovich Prokushkin, Alexey Vasilevich Panov, and Martin Jung
Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, https://doi.org/10.5194/bg-19-2805-2022, 2022
Short summary
Short summary
Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Elisabeth Mauclet, Yannick Agnan, Catherine Hirst, Arthur Monhonval, Benoît Pereira, Aubry Vandeuren, Maëlle Villani, Justin Ledman, Meghan Taylor, Briana L. Jasinski, Edward A. G. Schuur, and Sophie Opfergelt
Biogeosciences, 19, 2333–2351, https://doi.org/10.5194/bg-19-2333-2022, https://doi.org/10.5194/bg-19-2333-2022, 2022
Short summary
Short summary
Arctic warming and permafrost degradation largely affect tundra vegetation. Wetter lowlands show an increase in sedges, whereas drier uplands favor shrub expansion. Here, we demonstrate that the difference in the foliar elemental composition of typical tundra vegetation species controls the change in local foliar elemental stock and potential mineral element cycling through litter production upon a shift in tundra vegetation.
Tiexi Chen, Renjie Guo, Qingyun Yan, Xin Chen, Shengjie Zhou, Chuanzhuang Liang, Xueqiong Wei, and Han Dolman
Biogeosciences, 19, 1515–1525, https://doi.org/10.5194/bg-19-1515-2022, https://doi.org/10.5194/bg-19-1515-2022, 2022
Short summary
Short summary
Currently people are very concerned about vegetation changes and their driving factors, including natural and anthropogenic drivers. In this study, a general browning trend is found in Syria during 2001–2018, indicated by the vegetation index. We found that land management caused by social unrest is the main cause of this browning phenomenon. The mechanism initially reported here highlights the importance of land management impacts at the regional scale.
Rahayu Adzhar, Douglas I. Kelley, Ning Dong, Charles George, Mireia Torello Raventos, Elmar Veenendaal, Ted R. Feldpausch, Oliver L. Phillips, Simon L. Lewis, Bonaventure Sonké, Herman Taedoumg, Beatriz Schwantes Marimon, Tomas Domingues, Luzmila Arroyo, Gloria Djagbletey, Gustavo Saiz, and France Gerard
Biogeosciences, 19, 1377–1394, https://doi.org/10.5194/bg-19-1377-2022, https://doi.org/10.5194/bg-19-1377-2022, 2022
Short summary
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
Short summary
Short summary
The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Anam M. Khan, Paul C. Stoy, James T. Douglas, Martha Anderson, George Diak, Jason A. Otkin, Christopher Hain, Elizabeth M. Rehbein, and Joel McCorkel
Biogeosciences, 18, 4117–4141, https://doi.org/10.5194/bg-18-4117-2021, https://doi.org/10.5194/bg-18-4117-2021, 2021
Short summary
Short summary
Remote sensing has played an important role in the study of land surface processes. Geostationary satellites, such as the GOES-R series, can observe the Earth every 5–15 min, providing us with more observations than widely used polar-orbiting satellites. Here, we outline current efforts utilizing geostationary observations in environmental science and look towards the future of GOES observations in the carbon cycle, ecosystem disturbance, and other areas of application in environmental science.
Lydia Stolpmann, Caroline Coch, Anne Morgenstern, Julia Boike, Michael Fritz, Ulrike Herzschuh, Kathleen Stoof-Leichsenring, Yury Dvornikov, Birgit Heim, Josefine Lenz, Amy Larsen, Katey Walter Anthony, Benjamin Jones, Karen Frey, and Guido Grosse
Biogeosciences, 18, 3917–3936, https://doi.org/10.5194/bg-18-3917-2021, https://doi.org/10.5194/bg-18-3917-2021, 2021
Short summary
Short summary
Our new database summarizes DOC concentrations of 2167 water samples from 1833 lakes in permafrost regions across the Arctic to provide insights into linkages between DOC and environment. We found increasing lake DOC concentration with decreasing permafrost extent and higher DOC concentrations in boreal permafrost sites compared to tundra sites. Our study shows that DOC concentration depends on the environmental properties of a lake, especially permafrost extent, ecoregion, and vegetation.
Gustaf Granath, Christopher D. Evans, Joachim Strengbom, Jens Fölster, Achim Grelle, Johan Strömqvist, and Stephan J. Köhler
Biogeosciences, 18, 3243–3261, https://doi.org/10.5194/bg-18-3243-2021, https://doi.org/10.5194/bg-18-3243-2021, 2021
Short summary
Short summary
We measured element losses and impacts on water quality following a wildfire in Sweden. We observed the largest carbon and nitrogen losses during the fire and a strong pulse of elements 1–3 months after the fire that showed a fast (weeks) and a slow (months) release from the catchments. Total carbon export through water did not increase post-fire. Overall, we observed a rapid recovery of the biogeochemical cycling of elements within 3 years but still an annual net release of carbon dioxide.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203, https://doi.org/10.5194/bg-18-2181-2021, https://doi.org/10.5194/bg-18-2181-2021, 2021
Short summary
Short summary
The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Matthias Volk, Matthias Suter, Anne-Lena Wahl, and Seraina Bassin
Biogeosciences, 18, 2075–2090, https://doi.org/10.5194/bg-18-2075-2021, https://doi.org/10.5194/bg-18-2075-2021, 2021
Short summary
Short summary
Grassland ecosystem services like forage production and greenhouse gas storage in the soil depend on plant growth.
In an experiment in the mountains with warming treatments, we found that despite dwindling soil water content, the grassland growth increased with up to +1.3 °C warming (annual mean) compared to present temperatures. Even at +2.4 °C the growth was still larger than at the reference site.
This suggests that plant growth will increase due to global warming in the near future.
Bernice C. Hwang and Daniel B. Metcalfe
Biogeosciences, 18, 1259–1268, https://doi.org/10.5194/bg-18-1259-2021, https://doi.org/10.5194/bg-18-1259-2021, 2021
Short summary
Short summary
Despite growing recognition of herbivores as important ecosystem engineers, many major gaps remain in our understanding of how silicon and herbivory interact to shape biogeochemical processes. We highlight the need for more research particularly in natural settings as well as on the potential effects of herbivory on terrestrial silicon cycling to understand potentially critical animal–plant–soil feedbacks.
Ali Asaadi and Vivek K. Arora
Biogeosciences, 18, 669–706, https://doi.org/10.5194/bg-18-669-2021, https://doi.org/10.5194/bg-18-669-2021, 2021
Short summary
Short summary
More than a quarter of the current anthropogenic CO2 emissions are taken up by land, reducing the atmospheric CO2 growth rate. This is because of the CO2 fertilization effect which benefits 80 % of global vegetation. However, if nitrogen and phosphorus nutrients cannot keep up with increasing atmospheric CO2, the magnitude of this terrestrial ecosystem service may reduce in future. This paper implements nitrogen constraints on photosynthesis in a model to understand the mechanisms involved.
Arianna Peron, Lisa Kaser, Anne Charlott Fitzky, Martin Graus, Heidi Halbwirth, Jürgen Greiner, Georg Wohlfahrt, Boris Rewald, Hans Sandén, and Thomas Karl
Biogeosciences, 18, 535–556, https://doi.org/10.5194/bg-18-535-2021, https://doi.org/10.5194/bg-18-535-2021, 2021
Short summary
Short summary
Drought events are expected to become more frequent with climate change. Along with these events atmospheric ozone is also expected to increase. Both can stress plants. Here we investigate to what extent these factors modulate the emission of volatile organic compounds (VOCs) from oak plants. We find an antagonistic effect between drought stress and ozone, impacting the emission of different BVOCs, which is indirectly controlled by stomatal opening, allowing plants to control their water budget.
Lena Wohlgemuth, Stefan Osterwalder, Carl Joseph, Ansgar Kahmen, Günter Hoch, Christine Alewell, and Martin Jiskra
Biogeosciences, 17, 6441–6456, https://doi.org/10.5194/bg-17-6441-2020, https://doi.org/10.5194/bg-17-6441-2020, 2020
Short summary
Short summary
Mercury uptake by trees from the air represents an important but poorly quantified pathway in the global mercury cycle. We determined mercury uptake fluxes by leaves and needles at 10 European forests which were 4 times larger than mercury deposition via rainfall. The amount of mercury taken up by leaves and needles depends on their age and growing height on the tree. Scaling up our measurements to the forest area of Europe, we estimate that each year 20 t of mercury is taken up by trees.
A. Anthony Bloom, Kevin W. Bowman, Junjie Liu, Alexandra G. Konings, John R. Worden, Nicholas C. Parazoo, Victoria Meyer, John T. Reager, Helen M. Worden, Zhe Jiang, Gregory R. Quetin, T. Luke Smallman, Jean-François Exbrayat, Yi Yin, Sassan S. Saatchi, Mathew Williams, and David S. Schimel
Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, https://doi.org/10.5194/bg-17-6393-2020, 2020
Short summary
Short summary
We use a model of the 2001–2015 tropical land carbon cycle, with satellite measurements of land and atmospheric carbon, to disentangle lagged and concurrent effects (due to past and concurrent meteorological events, respectively) on annual land–atmosphere carbon exchanges. The variability of lagged effects explains most 2001–2015 inter-annual carbon flux variations. We conclude that concurrent and lagged effects need to be accurately resolved to better predict the world's land carbon sink.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Taraka Davies-Barnard, Johannes Meyerholt, Sönke Zaehle, Pierre Friedlingstein, Victor Brovkin, Yuanchao Fan, Rosie A. Fisher, Chris D. Jones, Hanna Lee, Daniele Peano, Benjamin Smith, David Wårlind, and Andy J. Wiltshire
Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, https://doi.org/10.5194/bg-17-5129-2020, 2020
Rui Cheng, Troy S. Magney, Debsunder Dutta, David R. Bowling, Barry A. Logan, Sean P. Burns, Peter D. Blanken, Katja Grossmann, Sophia Lopez, Andrew D. Richardson, Jochen Stutz, and Christian Frankenberg
Biogeosciences, 17, 4523–4544, https://doi.org/10.5194/bg-17-4523-2020, https://doi.org/10.5194/bg-17-4523-2020, 2020
Short summary
Short summary
We measured reflected sunlight from an evergreen canopy for a year to detect changes in pigments that play an important role in regulating the seasonality of photosynthesis. Results show a strong mechanistic link between spectral reflectance features and pigment content, which is validated using a biophysical model. Our results show spectrally where, why, and when spectral features change over the course of the season and show promise for estimating photosynthesis remotely.
Jarmo Mäkelä, Francesco Minunno, Tuula Aalto, Annikki Mäkelä, Tiina Markkanen, and Mikko Peltoniemi
Biogeosciences, 17, 2681–2700, https://doi.org/10.5194/bg-17-2681-2020, https://doi.org/10.5194/bg-17-2681-2020, 2020
Short summary
Short summary
We assess the relative magnitude of uncertainty sources on ecosystem indicators of the 21st century climate change on two boreal forest sites. In addition to RCP and climate model uncertainties, we included the overlooked model parameter uncertainty and management actions in our analysis. Management was the dominant uncertainty factor for the more verdant southern site, followed by RCP, climate and parameter uncertainties. The uncertainties were estimated with canonical correlation analysis.
Guido Kraemer, Gustau Camps-Valls, Markus Reichstein, and Miguel D. Mahecha
Biogeosciences, 17, 2397–2424, https://doi.org/10.5194/bg-17-2397-2020, https://doi.org/10.5194/bg-17-2397-2020, 2020
Short summary
Short summary
To closely monitor the state of our planet, we require systems that can monitor
the observation of many different properties at the same time. We create
indicators that resemble the behavior of many different simultaneous
observations. We apply the method to create indicators representing the
Earth's biosphere. The indicators show a productivity gradient and a water
gradient. The resulting indicators can detect a large number of changes and
extremes in the Earth system.
Barbara Marcolla, Mirco Migliavacca, Christian Rödenbeck, and Alessandro Cescatti
Biogeosciences, 17, 2365–2379, https://doi.org/10.5194/bg-17-2365-2020, https://doi.org/10.5194/bg-17-2365-2020, 2020
Short summary
Short summary
This work investigates the sensitivity of terrestrial CO2 fluxes to climate drivers. We observed that CO2 flux is mostly controlled by temperature during the growing season and by radiation off season. We also observe that radiation importance is increasing over time while sensitivity to temperature is decreasing in Eurasia. Ultimately this analysis shows that ecosystem response to climate is changing, with potential repercussions for future terrestrial sink and land role in climate mitigation.
Stephanie C. Pennington, Nate G. McDowell, J. Patrick Megonigal, James C. Stegen, and Ben Bond-Lamberty
Biogeosciences, 17, 771–780, https://doi.org/10.5194/bg-17-771-2020, https://doi.org/10.5194/bg-17-771-2020, 2020
Short summary
Short summary
Soil respiration (Rs) is the flow of CO2 from the soil surface to the atmosphere and is one of the largest carbon fluxes on land. This study examined the effect of local basal area (tree area) on Rs in a coastal forest in eastern Maryland, USA. Rs measurements were taken as well as distance from soil collar, diameter, and species of each tree within a 15 m radius. We found that trees within 5 m of our sampling points had a positive effect on how sensitive soil respiration was to temperature.
Keri L. Bowering, Kate A. Edwards, Karen Prestegaard, Xinbiao Zhu, and Susan E. Ziegler
Biogeosciences, 17, 581–595, https://doi.org/10.5194/bg-17-581-2020, https://doi.org/10.5194/bg-17-581-2020, 2020
Short summary
Short summary
We examined the effects of season and tree harvesting on the flow of water and the organic carbon (OC) it carries from boreal forest soils. We found that more OC was lost from the harvested forest because more precipitation reached the soil surface but that during periods of flushing in autumn and snowmelt a limit on the amount of water-extractable OC is reached. These results contribute to an increased understanding of carbon loss from boreal forest soils.
Jason Philip Kaye, Susan L. Brantley, Jennifer Zan Williams, and the SSHCZO team
Biogeosciences, 16, 4661–4669, https://doi.org/10.5194/bg-16-4661-2019, https://doi.org/10.5194/bg-16-4661-2019, 2019
Short summary
Short summary
Interdisciplinary teams can only capitalize on innovative ideas if members work well together through collegial and efficient use of field sites, instrumentation, samples, data, and model code. Thus, biogeoscience teams may benefit from developing a set of best practices for collaboration. We present one such example from a the Susquehanna Shale Hills critical zone observatory. Many of the themes from our example are universal, and they offer insights useful to other biogeoscience teams.
Anne Alexandre, Elizabeth Webb, Amaelle Landais, Clément Piel, Sébastien Devidal, Corinne Sonzogni, Martine Couapel, Jean-Charles Mazur, Monique Pierre, Frédéric Prié, Christine Vallet-Coulomb, Clément Outrequin, and Jacques Roy
Biogeosciences, 16, 4613–4625, https://doi.org/10.5194/bg-16-4613-2019, https://doi.org/10.5194/bg-16-4613-2019, 2019
Short summary
Short summary
This calibration study shows that despite isotope heterogeneity along grass leaves, the triple oxygen isotope composition of bulk leaf phytoliths can be estimated from the Craig and Gordon model, a mixing equation and a mean leaf water–phytolith fractionation exponent (lambda) of 0.521. The results strengthen the reliability of the 17O–excess of phytoliths to be used as a proxy of atmospheric relative humidity and open tracks for its use as an imprint of leaf water 17O–excess.
Lina Teckentrup, Sandy P. Harrison, Stijn Hantson, Angelika Heil, Joe R. Melton, Matthew Forrest, Fang Li, Chao Yue, Almut Arneth, Thomas Hickler, Stephen Sitch, and Gitta Lasslop
Biogeosciences, 16, 3883–3910, https://doi.org/10.5194/bg-16-3883-2019, https://doi.org/10.5194/bg-16-3883-2019, 2019
Short summary
Short summary
This study compares simulated burned area of seven global vegetation models provided by the Fire Model Intercomparison Project (FireMIP) since 1900. We investigate the influence of five forcing factors: atmospheric CO2, population density, land–use change, lightning and climate.
We find that the anthropogenic factors lead to the largest spread between models. Trends due to climate are mostly not significant but climate strongly influences the inter-annual variability of burned area.
Marcos A. S. Scaranello, Michael Keller, Marcos Longo, Maiza N. dos-Santos, Veronika Leitold, Douglas C. Morton, Ekena R. Pinagé, and Fernando Del Bon Espírito-Santo
Biogeosciences, 16, 3457–3474, https://doi.org/10.5194/bg-16-3457-2019, https://doi.org/10.5194/bg-16-3457-2019, 2019
Short summary
Short summary
The coarse dead wood component of the tropical forest carbon pool is rarely measured. For the first time, we developed models for predicting coarse dead wood in Amazonian forests by using airborne laser scanning data. Our models produced site-based estimates similar to independent field estimates found in the literature. Our study provides an approach for estimating coarse dead wood pools from remotely sensed data and mapping those pools over large scales in intact and degraded forests.
James Brennan, Jose L. Gómez-Dans, Mathias Disney, and Philip Lewis
Biogeosciences, 16, 3147–3164, https://doi.org/10.5194/bg-16-3147-2019, https://doi.org/10.5194/bg-16-3147-2019, 2019
Short summary
Short summary
We estimate the uncertainties associated with three global satellite-derived burned area estimates. The method provides unique uncertainties for the three estimates at the global scale for 2001–2013. We find uncertainties of 4 %–5.5 % in global burned area and uncertainties of 8 %–10 % in the frequently burning regions of Africa and Australia.
Alexander J. Norton, Peter J. Rayner, Ernest N. Koffi, Marko Scholze, Jeremy D. Silver, and Ying-Ping Wang
Biogeosciences, 16, 3069–3093, https://doi.org/10.5194/bg-16-3069-2019, https://doi.org/10.5194/bg-16-3069-2019, 2019
Short summary
Short summary
This study presents an estimate of global terrestrial photosynthesis. We make use of satellite chlorophyll fluorescence measurements, a visible indicator of photosynthesis, to optimize model parameters and estimate photosynthetic carbon uptake. This new framework incorporates nonlinear, process-based understanding of the link between fluorescence and photosynthesis, an advance on past approaches. This will aid in the utility of fluorescence to quantify terrestrial carbon cycle feedbacks.
Cited articles
Aber, J. D.: Why don't we believe the models?, Bull. Ecol.
Soc. Am., 78, 232–233, 1997. a
Abramoff, R., Xu, X., Hartman, M., O’Brien, S., Feng, W., Davidson, E.,
Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The
Millennial model: in search of measurable pools and transformations for
modeling soil carbon in the new century, Biogeochemistry, 137, 51–71,
https://doi.org/10.1007/s10533-017-0409-7, 2018. a
Baldock, J. A., Wheeler, I., McKenzie, N., and McBrateny, A.: Soils and climate
change: potential impacts on carbon stocks and greenhouse gas emissions, and
future research for Australian agriculture, Crop Past. Sci., 63,
269–283, https://doi.org/10.1071/cp11170, 2012. a
Batjes, N. H.: Total carbon and nitrogen in the soils of the world, Europ.
J. Soil Sci., 47, 151–163, https://doi.org/10.1111/j.1365-2389.1996.tb01386.x, 1996. a
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised soil profile data
to support global mapping and modelling (WoSIS snapshot 2019), Earth Syst.
Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020. a
Blankinship, J. C., Berhe, A. A., Crow, S. E., Druhan, J. L., Heckman, K. A.,
Keiluweit, M., Lawrence, C. R., Marin-Spiotta, E., Plante, A. F., Rasmussen,
C., Schadel, C., Schimel, J. P., Sierra, C. A., Thompson, A., Wagai, R., and
Wieder, W. R.: Improving understanding of soil organic matter dynamics by
triangulating theories, measurements, and models, Biogeochemistry, 140,
1–13, https://doi.org/10.1007/s10533-018-0478-2, 2018. a, b
Bolinder, M. A., Angers, D. A., and Dubuc, J. P.: Estimating shoot to root
ratios and annual carbon inputs in soils for cereal crops, Agr.
Ecosyst. Environ., 63, 61–66, https://doi.org/10.1016/S0167-8809(96)01121-8,
1997. a
BOM: The Köppen classification maps, australian Bureau of Meteorology, available at:
http://www.bom.gov.au/climate/ (last access: 1 June 2019), 2016. a
Campbell, E. E. and Paustian, K.: Current developments in soil organic matter
modeling and the expansion of model applications: a review, Environ.
Res. Lett., 10, 123004, https://doi.org/10.1088/1748-9326/10/12/123004, 2015. a, b
Ciais, P., Paris, J. D., Marland, G., Peylin, P., Piao, S. L., Levin, I.,
Pregger, T., Scholz, Y., Friedrich, R., Rivier, L., Houwelling, S., Schulze,
E. D., and Members of the CARBOEUROPE Synthesis Team: The European carbon balance, Part 1:
fossil fuel emissions, Glob. Change Biol., 16, 1395–1408,
https://doi.org/10.1111/j.1365-2486.2009.02098.x, 2010. a
Conant, R. T., Ogle, S. M., Paul, E. A., and Paustian, K.: Measuring and
monitoring soil organic carbon stocks in agricultural lands for climate
mitigation, Front. Ecol. Environ., 9, 169–173,
https://doi.org/10.1890/090153, 2011. a, b
Conover, W. J.: Practical Nonparametric Statistics, 3rd Edn., John Wiley and
Sons, New York, 1998. a
DPIRD: Department of Primary Industries and Regional Development, Grazing
annual pastures – using feed on offer (FOO) as a guide, available at:
https://www.agric.wa.gov.au/pasture-management/grazing-annual-pastures-%E2%80%93-using-feed-offer-foo-guide , last access: 20 September 2021. a
England, J. R. and Viscarra Rossel, R. A.: Proximal sensing for soil carbon
accounting, SOIL, 4, 101–122, https://doi.org/10.5194/soil-4-101-2018, 2018. a, b, c
Falloon, P. D. and Smith, P.: Modelling refractory soil organic matter, Biol. Fert. Soil., 30, 388–398, https://doi.org/10.1007/s003740050019, 2000. a
Farina, R., Coleman, K., and Whitmore, A. P.: Modification of the RothC model
for simulations of soil organic C dynamics in dryland regions, Geoderma,
200–201, 18–30, https://doi.org/10.1016/j.geoderma.2013.01.021, 2013. a, b
Gifford, R. M.: Carbon sequestration in Australian grasslands: policy and
technical issues, Tech. Rep., 2010. a
Grunwald, S., Thompson, J. A., and Boettinger, J. L.: Digital Soil Mapping and
Modeling at Continental Scales: Finding Solutions for Global Issues, Soil
Sci. Soc. Am. J., 75, 1201–1213,
https://doi.org/10.2136/sssaj2011.0025, 2011. a
Harden, J. W., Hugelius, G., Ahlström, A., Blankinship, J. C.,
Bond-Lamberty, B., Lawrence, C. R., Loisel, J., Malhotra, A., Jackson, R. B.,
Ogle, S., Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara,
S. E., Cotrufo, M. F., Keiluweit, M., Heckman, K. A., Crow, S. E., Silver,
W. L., DeLonge, M., and Nave, L. E.: Networking our science to characterize
the state, vulnerabilities, and management opportunities of soil organic
matter, Glob. Change Biol., 24, e705–e718, https://doi.org/10.1111/gcb.13896, 2018. a, b
Hargreaves, G. and Samani, Z.: Estimating potential evapotranspiration, J. Irr. Drain. Eng., 108, 223–230,
https://doi.org/10.1061/JRCEA4.0001390, 1982. a
Hastie, T., Tibshirani, R., and Friedman, J.: Model Assessment and Selection,
in: The Elements of Statistical Learning Data Mining, Inference, and
Prediction, Second Edition, chap. 7, Springer-Verlag New York, 219–260,
2009. a
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Isaac, P.,
Pickett-Heaps, C., Roxburgh, S. H., van Gorsel, E., Rossel, R. A. V., and
Wang, Z.: Multiple observation types reduce uncertainty in Australia's
terrestrial carbon and water cycles, Biogeosciences, 10, 2011–2040,
https://doi.org/10.5194/bg-10-2011-2013, 2013. a
Haverd, V., Smith, B., and Trudinger, C.: Process contributions of Australian
ecosystems to interannual variations in the carbon cycle, Environ.
Res. Lett., 11, 054013, https://doi.org/10.1088/1748-9326/11/5/054013, 2016. a
Hoyle, F. C., D'Antuono, M., Overheu, T., and Murphy, D. V.: Capacity for
increasing soil organic carbon stocks in dryland agricultural systems, Soil
Res., 51, 657–667, https://doi.org/10.1071/Sr12373, 2013. a
Jenkinson, D. S.: The turnover of organic carbon and nitrogen in soil,
Philos. T. R. Soc. Lond. Ser. B, 329, 361–368, https://doi.org/10.1098/rstb.1990.0177, 1990. a, b
Jenkinson, D. S. and Rayner, J. H.: The turnover of soil organic matter in some
of the Rothamsted classical experiments, Soil Sci., 123, 298–305,
https://doi.org/10.1097/00010694-197705000-00005, 1977. a
Krull, E. S., Baldock, J. A., and Skjemstad, J. O.: Importance of mechanisms
and processes of the stabilisation of soil organic matter for modelling
carbon turnover, Funct. Plant Biol., 30, 207–222, 2003. a
Lal, R.: Beyond COP21: Potential and challenges of the “4 per Thousand”
initiative, J. Soil Water Conserv., 71, 20a–25a,
https://doi.org/10.2489/jswc.71.1.20A, 2016. a, b
Lam, S. K., Chen, D., Mosier, A. R., and Roush, R.: The potential for carbon
sequestration in Australian agricultural soils is technically and
economically limited, Sci. Rep., 3, 2179, https://doi.org/10.1038/srep02179,
2013. a
Lavallee, J. M., Soong, J. L., and Cotrufo, M. F.: Conceptualizing soil organic
matter into particulate and mineral-associated forms to address global change
in the 21st century, Glob. Change Biol., 26, 261–273,
https://doi.org/10.1111/gcb.14859, 2020. a
Lee, J. and Viscarra Rossel, R. A.: Soil carbon simulation confounded by
different pool initialisation, Nutr. Cy. Agroecosyst., 116,
245–255, https://doi.org/10.1007/s10705-019-10041-0, 2020. a, b
Lehmann, J. and Kleber, M.: The contentious nature of soil organic matter,
Nature, 528, 60–68, https://doi.org/10.1038/nature16069, 2015. a
Li, Z., Liu, S., Tan, Z., Bliss, N. B., Young, C. J., West, T. O., and Ogle,
S. M.: Comparing cropland net primary production estimates from inventory, a
satellite-based model, and a process-based model in the Midwest of the United
States, Ecol. Model., 277, 1–12,
https://doi.org/10.1016/j.ecolmodel.2014.01.012, 2014. a
Luo, Z. K., Wang, E. L., Baldock, J., and Xing, H. T.: Potential soil organic
carbon stock and its uncertainty under various cropping systems in Australian
cropland, Soil Res., 52, 463–475, https://doi.org/10.1071/Sr13294, 2014. a
Luo, Z. K., Feng, W. T., Luo, Y. Q., Baldock, J., and Wang, E. L.: Soil organic
carbon dynamics jointly controlled by climate, carbon inputs, soil properties
and soil carbon fractions, Glob. Change Biol., 23, 4430–4439,
https://doi.org/10.1111/gcb.13767, 2017. a
Maillard, E. and Angers, D. A.: Animal manure application and soil organic
carbon stocks: a meta-analysis, Glob. Change Biol., 20, 666–679,
https://doi.org/10.1111/gcb.12438, 2014. a
Manzoni, S. and Porporato, A.: Soil carbon and nitrogen mineralization: Theory
and models across scales, Soil Biol. Biochem., 41, 1355–1379,
https://doi.org/10.1016/j.soilbio.2009.02.031, 2009. a
Metting, F. B., Smith, J. L., Amthor, J. S., and Izaurralde, R. C.: Science
needs and new technology for increasing soil carbon sequestration, Climatic
Change, 51, 11–34, https://doi.org/10.1023/a:1017509224801, 2001. a
Minty, B., Franklin, R., Milligan, P., Richardson, M., and Wilford, J.: The
radiometric map of Australia, Expl. Geophys., 40, 325–333,
https://doi.org/10.1071/Eg09025, 2009. a
MLA: Meat & Livestock Australia, Pasture Utilisation, available at:
https://mbfp.mla.com.au/pasture-utilisation/ (last access: 1 February 2020), 2019. a
Nix, H. A.: Simplified simulation models based on specified minimum data sets: the CROPEVAL concept, in: Application of Remote Sensing to Agricultural Production Forecasting, edited by: Berg, A., 151–169, A.A.Balkema, Rotterdam, 1981. a
Ogle, S. M., Breidt, F. J., Easter, M., Williams, S., Killian, K., and
Paustian, K.: Scale and uncertainty in modeled soil organic carbon stock
changes for US croplands using a process-based model, Glob. Change Biol., 16, 810–822,
https://doi.org/10.1111/j.1365-2486.2009.01951.x, 2010. a
O'Rourke, S. M., Angers, D. A., Holden, N. M., and McBratney, A. B.: Soil
organic carbon across scales, Glob. Change Biol., 21, 3561–3574,
https://doi.org/10.1111/gcb.12959, 2015. a
Pareek, A., Dhankher, O. P., and Foyer, C. H.: Mitigating the impact of
climate change on plant productivity and ecosystem sustainability, J. Exp. Bot., 71, 451–456, https://doi.org/10.1093/jxb/erz518, 2020. a
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.: Analysis of
factors controlling soil organic matter levels in Great Plains grasslands,
Soil Sci. Soc. Am. J., 51, 1173–1179,
https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987. a
Paul, K. I. and Polglase, P. J.: Calibration of the RothC model to turnover of
soil carbon under eucalypts and pines, Austr. J. Soil Res.,
42, 883–895, https://doi.org/10.1071/Sr04025, 2004. a
Paustian, K., Levine, E., Post, W. M., and Ryzhova, I. M.: The use of models to
integrate information and understanding of soil C at the regional scale,
Geoderma, 79, 227–260, https://doi.org/10.1016/S0016-7061(97)00043-8, 1997. a
Paustian, K., Larson, E., Kent, J., Marx, E., and Swan, A.: Soil C
sequestration as a biological negative emission strategy, Front.
Clim., 1, 8, https://doi.org/10.3389/fclim.2019.00008, 2019. a, b, c, d
Poeplau, C. and Don, A.: Carbon sequestration in agricultural soils via
cultivation of cover crops – A meta-analysis, Agriculture, Ecosyst.
Environ., 200, 33–41, https://doi.org/10.1016/j.agee.2014.10.024,
2015. a
Post, W. M. and Kwon, K. C.: Soil carbon sequestration and land-use change:
Processes and potential, Glob. Change Biol., 6, 317–327, 2000. a
Richards, G. P. and Evans, D. M. W.: Development of a carbon accounting model
(FullCAM Vers. 1.0) for the Australian continent, Aust. Forestry, 67,
277–283, https://doi.org/10.1080/00049158.2004.10674947, 2004. a, b
Robertson, A. D., Paustian, K., Ogle, S., Wallenstein, M. D., Lugato, E., and
Cotrufo, M. F.: Unifying soil organic matter formation and persistence
frameworks: the MEMS model, Biogeosciences, 16, 1225–1248,
https://doi.org/10.5194/bg-16-1225-2019, 2019. a
Roxburgh, S. H., Berry, S. L., Buckley, T. N., Barnes, B., and Roderick, M. L.:
What is NPP? Inconsistent accounting of respiratory fluxes in the definition
of net primary production, Funct. Ecol., 19, 378–382,
https://doi.org/10.1111/j.1365-2435.2005.00983.x, 2005. a
Scharlemann, J. P. W., Tanner, E. V. J., Hiederer, R., and Kapos, V.: Global
soil carbon: understanding and managing the largest terrestrial carbon pool,
Carbon Manag., 5, 81–91, https://doi.org/10.4155/Cmt.13.77, 2014. a
Seidel, S. J., Palosuo, T., Thorburn, P., and Wallach, D.: Towards improved
calibration of crop models – Where are we now and where should we go?,
Europ. J. Agron., 94, 25–35,
https://doi.org/10.1016/j.eja.2018.01.006, 2018. a, b
Shi, Z., Crowell, S., Luo, Y. Q., and Moore, B.: Model structures amplify
uncertainty in predicted soil carbon responses to climate change, Nat.
Commun., 9, 11, 2171, https://doi.org/10.1038/s41467-018-04526-9, 2018. a
SILO: Australian Climate Database, Queensland Department of Environment
and Science, available at: https://www.longpaddock.qld.gov.au/silo/, last access: 20 September 2021. a
Skjemstad, J. and Spouncer, L.: Integrated Soils Modelling for the National
Carbon Accounting System, National Carbon Accounting System Technical Report
No. 36, Tech. Rep., The Australian Greenhouse Office, 2003. a
Smith, P., Smith, J., Powlson, D., McGill, W., Arah, J., Chertov, O., Coleman,
K., Franko, U., Frolking, S., Jenkinson, D., Jensen, L., Kelly, R.,
Klein-Gunnewiek, H., Komarov, A., Li, C., Molina, J., Mueller, T., Parton,
W., Thornley, J., and Whitmore, A.: A comparison of the performance of nine
soil organic matter models using datasets from seven long-term experiments,
Geoderma, 81, 153–225, https://doi.org/10.1016/S0016-7061(97)00087-6, 1997. a
Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D. P.,
Batjes, N. H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navarro, C.,
Olesen, J. E., Chirinda, N., Fornara, D., Wollenberg, E., Álvaro Fuentes,
J., Sanz-Cobena, A., and Klumpp, K.: How to measure, report and verify soil
carbon change to realize the potential of soil carbon sequestration for
atmospheric greenhouse gas removal, Glob. Change Biol., 26, 219–241,
https://doi.org/10.1111/gcb.14815, 2020. a, b, c, d
Sparling, G.: Ratio of microbial biomass carbon to soil organic carbon as a
sensitive indicator of changes in soil organic matter, Soil Res., 30,
195–207, https://doi.org/10.1071/SR9920195, 1992. a
Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein,
P., Knutti, R., Lowe, J., O'Neill, B., Sanderson, B., van Vuuren, D., Riahi,
K., Meinshausen, M., Nicholls, Z., Tokarska, K. B., Hurtt, G., Kriegler, E.,
Lamarque, J.-F., Meehl, G., Moss, R., Bauer, S. E., Boucher, O., Brovkin, V.,
Byun, Y.-H., Dix, M., Gualdi, S., Guo, H., John, J. G., Kharin, S., Kim, Y.,
Koshiro, T., Ma, L., Olivié, D., Panickal, S., Qiao, F., Rong, X.,
Rosenbloom, N., Schupfner, M., Séférian, R., Sellar, A., Semmler, T.,
Shi, X., Song, Z., Steger, C., Stouffer, R., Swart, N., Tachiiri, K., Tang,
Q., Tatebe, H., Voldoire, A., Volodin, E., Wyser, K., Xin, X., Yang, S., Yu,
Y., and Ziehn, T.: Climate model projections from the Scenario Model
Intercomparison Project (ScenarioMIP) of CMIP6, Earth Syst. Dynam., 12,
253–293, https://doi.org/10.5194/esd-12-253-2021, 2021. a, b
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai,
C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon
simulations from CMIP5 Earth system models and comparison with observations,
Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013. a
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Running, S. W., Zhao,
M. S., Costa, M. H., Kirschbaum, A. A., Ham, J. M., Saleska, S. R., and Ahl,
D. E.: Evaluation of MODIS NPP and GPP products across multiple biomes,
Remote Sens. Environ., 102, 282–292,
https://doi.org/10.1016/j.rse.2006.02.017, 2006. a
Unkovich, M., Baldock, J., and Farquharson, R.: Field measurements of bare
soil evaporation and crop transpiration, and transpiration efficiency, for
rainfed grain crops in Australia – A review, Agr. Water
Manag., 205, 72–80, https://doi.org/10.1016/j.agwat.2018.04.016, 2018. a
Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, D.
O. T., Vanderborght, J., Young, M. H., Amelung, W., Aitkenhead, M., Allison,
S. D., Assouline, S., Baveye, P., Berli, M., Bruggemann, N., Finke, P.,
Flury, M., Gaiser, T., Govers, G., Ghezzehei, T., Hallett, P., Franssen, H.
J. H., Heppell, J., Horn, R., Huisman, J. A., Jacques, D., Jonard, F.,
Kollet, S., Lafolie, F., Lamorski, K., Leitner, D., McBratney, A., Minasny,
B., Montzka, C., Nowak, W., Pachepsky, Y., Padarian, J., Romano, N., Roth,
K., Rothfuss, Y., Rowe, E. C., Schwen, A., Simunek, J., Tiktak, A., Van Dam,
J., van der Zee, S. E. A. T. M., Vogel, H. J., Vrugt, J. A., Wohling, T., and
Young, I. M.: Modeling soil processes: Review, key challenges, and new
perspectives, Vadose Zone J., 15, 1–57, https://doi.org/10.2136/vzj2015.09.0131,
2016. a
Viscarra Rossel, R. A.: Fine-resolution multiscale mapping of clay minerals in
Australian soils measured with near infrared spectra, J. Geophys.
Res.-Earth Surf., 116, F04023, https://doi.org/10.1029/2011jf001977, 2011. a
Viscarra Rossel, R. A. and Brus, D. J.: The cost-efficiency and reliability of
two methods for soil organic C accounting, Land Degrad. Dev.,
29, 506–520, https://doi.org/10.1002/ldr.2887, 2018. a
Viscarra Rossel, R. A. and Webster, R.: Predicting soil properties from the
Australian soil visible-near infrared spectroscopic database, Europ.
J. Soil Sci., 63, 848–860,
https://doi.org/10.1111/j.1365-2389.2012.01495.x, 2012. a
Viscarra Rossel, R. A., Chen, C., Grundy, M. J., Searle, R., Clifford, D., and
Campbell, P. H.: The Australian three-dimensional soil grid: Australia’s
contribution to the GlobalSoilMap project, Soil Res., 53, 845–864,
https://doi.org/10.1071/SR14366, 2015. a, b, c
Wang, G. C., Luo, Z. K., Han, P. F., Chen, H. S., and Xu, J. J.: Critical
carbon input to maintain current soil organic carbon stocks in global wheat
systems, Sci. Rep., 6, 19327, https://doi.org/10.1038/srep19327, 2016. a, b, c
Wang, Y. P. and Barrett, D. J.: Estimating regional terrestrial carbon fluxes
for the Australian continent using a multiple-constraint approach I. Using
remotely sensed data and ecological observations of net primary production,
Tellus B, 55, 270–289,
https://doi.org/10.3402/tellusb.v55i2.16706, 2003.
a
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.: Integrating
microbial physiology and physio-chemical principles in soils with the
MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11,
3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014. a
Short summary
We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
We performed Roth C simulations across Australia and assessed the response of soil carbon to...
Altmetrics
Final-revised paper
Preprint